Search results for "Computer Science::Artificial Intelligence"

showing 5 items of 15 documents

Quasi conjunction, quasi disjunction, t-norms and t-conorms: Probabilistic aspects

2013

We make a probabilistic analysis related to some inference rules which play an important role in nonmonotonic reasoning. In a coherence-based setting, we study the extensions of a probability assessment defined on $n$ conditional events to their quasi conjunction, and by exploiting duality, to their quasi disjunction. The lower and upper bounds coincide with some well known t-norms and t-conorms: minimum, product, Lukasiewicz, and Hamacher t-norms and their dual t-conorms. On this basis we obtain Quasi And and Quasi Or rules. These are rules for which any finite family of conditional events p-entails the associated quasi conjunction and quasi disjunction. We examine some cases of logical de…

FOS: Computer and information sciencesSettore MAT/06 - Probabilita' E Statistica MatematicaInformation Systems and ManagementComputer Science - Artificial Intelligencet-Norms/conormDuality (mathematics)goodman-nguyen inclusion relation; lower/upper probability bounds; t-norms/conorms; generalized loop rule; coherence; quasi conjunction/disjunctionComputer Science::Artificial IntelligenceTheoretical Computer ScienceArtificial IntelligenceFOS: MathematicsProbabilistic analysis of algorithmsNon-monotonic logicRule of inferenceLower/upper probability boundGoodman–Nguyen inclusion relationMathematicsEvent (probability theory)Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniDiscrete mathematicsInterpretation (logic)Probability (math.PR)Probabilistic logicCoherence (philosophical gambling strategy)Generalized Loop ruleComputer Science ApplicationsAlgebraArtificial Intelligence (cs.AI)Control and Systems EngineeringQuasi conjunction/disjunctionCoherenceMathematics - ProbabilitySoftwareInformation Sciences
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On Carlson"s and Shafer"s inequalities

2014

In this paper the authors re ne the Carlson"s inequalities for inverse cosine function, and the Shafer"s inequalities for inverse tangent function.

InequalityApplied Mathematicsmedia_common.quotation_subjectCarlson's inequalityFunction (mathematics)Computer Science::Artificial IntelligenceAlgebraMathematics::LogicQA1-939Carlson"s inequality Shafer"s inequalityInverse trigonometric functionsShafer's inequalityArithmeticAnalysisMathematicsmedia_commonMathematicsinverse trigonometric functionsПроблемы анализа
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Transitivity in coherence-based probability logic

2016

We study probabilistically informative (weak) versions of transitivity by using suitable definitions of defaults and negated defaults in the setting of coherence and imprecise probabilities. We represent p-consistent sequences of defaults and/or negated defaults by g-coherent imprecise probability assessments on the respective sequences of conditional events. Moreover, we prove the coherent probability propagation rules for Weak Transitivity and the validity of selected inference patterns by proving p-entailment of the associated knowledge bases. Finally, we apply our results to study selected probabilistic versions of classical categorical syllogisms and construct a new version of the squa…

Square of oppositionSettore MAT/06 - Probabilita' E Statistica MatematicaTheoretical computer scienceLogicInferenceSquare of oppositionProbability logicSettore M-FIL/02 - Logica E Filosofia Della Scienza02 engineering and technologyComputer Science::Artificial Intelligence0603 philosophy ethics and religion0202 electrical engineering electronic engineering information engineeringGeneralized coherenceCategorical variableMathematicsTransitivityTransitive relationApplied MathematicsDefaultProbabilistic logicSyllogism06 humanities and the artsCoherence (statistics)Settore MAT/01 - Logica MatematicaImprecise probabilityp-EntailmentSyllogism060302 philosophyImprecise probabilityp-Consistency020201 artificial intelligence & image processingCoherenceAlgorithmJournal of Applied Logic
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Local Distance and Dempster-Dhafer for Multi-Focus Image Fusion

2022

This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing them to results of other methods.

[SPI] Engineering Sciences [physics]fusion imagesMulti-focus-imagesDempster-Shafer Theory[MATH] Mathematics [math]local distancefusion images.[INFO] Computer Science [cs]Computer Science::Artificial IntelligenceSignal & Image Processing : An International Journal
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Very narrow quantum OBDDs and width hierarchies for classical OBDDs

2014

In the paper we investigate a model for computing of Boolean functions - Ordered Binary Decision Diagrams (OBDDs), which is a restricted version of Branching Programs. We present several results on the comparative complexity for several variants of OBDD models. - We present some results on the comparative complexity of classical and quantum OBDDs. We consider a partial function depending on a parameter k such that for any k > 0 this function is computed by an exact quantum OBDD of width 2, but any classical OBDD (deterministic or stable bounded-error probabilistic) needs width 2 k+1. - We consider quantum and classical nondeterminism. We show that quantum nondeterminism can be more efficien…

nondeterminismFOS: Computer and information sciencespartial functionsGeneral Mathematicsquantum computation010102 general mathematics0102 computer and information sciencesOBDDComputational Complexity (cs.CC)Computer Science::Artificial IntelligenceComputer Science::Computational Complexity01 natural scienceswidth hierarchyComputer Science - Computational Complexity010201 computation theory & mathematicsComputer Science::Logic in Computer Science0101 mathematics
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